Digital Communications and Networks最新文献

A blockchain-based user-centric identity management toward 6G networks 面向6G网络的基于区块链的用户中心身份管理

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2026-01-01 DOI: 10.1016/j.dcan.2025.05.009

Guoqiang Zhang, Qiwei Hu, Yu Zhang, Tao Jiang

The developing Sixth-Generation (6G) network aims to establish seamless global connectivity for billions of humans, machines, and devices. However, the rich digital service and the explosive heterogeneous connection between various entities in 6G networks can not only induce increasing complications of digital identity management, but also raise material concerns about the security and privacy of the user identity. In this paper, we design a user-centric identity management that returns the sole control to the user self and achieves identity sovereignty toward 6G networks. Specifically, we propose a blockchain-based Identity Management (IDM) architecture for 6G networks, which provides a practical method to secure digital identity management. Subsequently, we develop a fully privacy-preserving identity attribute management scheme by using zero-knowledge proof to protect the privacy-sensitive identity attribute. In particular, the scheme achieves an identity attribute hiding and verification protocol to support users in obtaining and applying their identity attributes without revealing concrete data. Finally, we analyze the security of the proposed architecture and implement a prototype system to evaluate its performance. The results demonstrate that our architecture ensures effective user digital identity management in 6G networks.

正在发展的第六代（6G）网络旨在为数十亿人、机器和设备建立无缝的全球连接。然而，6G网络中丰富的数字业务和各种实体之间的爆炸性异构连接，不仅会导致数字身份管理的复杂性日益增加，而且会引发用户身份安全和隐私方面的实质性担忧。在本文中，我们设计了一种以用户为中心的身份管理，将唯一的控制权交还给用户自己，实现了对6G网络的身份主权。具体来说，我们提出了一种基于区块链的6G网络身份管理（IDM）架构，它提供了一种实用的方法来保护数字身份管理。随后，我们利用零知识证明来保护隐私敏感的身份属性，提出了一种完全保护隐私的身份属性管理方案。特别是，该方案实现了身份属性隐藏和验证协议，支持用户在不泄露具体数据的情况下获取和应用自己的身份属性。最后，我们分析了所提出的架构的安全性，并实现了一个原型系统来评估其性能。结果表明，我们的架构确保了6G网络中有效的用户数字身份管理。

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引用次数: 0

Cross-domain resources optimization for hybrid edge computing networks: Federated DRL approach 混合边缘计算网络的跨域资源优化：联合 DRL 方法

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2024.03.006

Xiaoqin Song , Quan Chen , Shumo Wang , Tiecheng Song

Due to the dynamic nature of service requests and the uneven distribution of services in the Internet of Vehicles (IoV), Multi-access Edge Computing (MEC) networks with pre-installed servers are often susceptible to insufficient computing power at certain times or in certain areas. In addition, Vehicular Users (VUs) need to share their observations for centralized neural network training, resulting in additional communication overhead. In this paper, we present a hybrid MEC server architecture, where fixed RoadSide Units (RSUs) and Mobile Edge Servers (MESs) cooperate to provide computation offloading services to VUs. We propose a distributed federated learning and Deep Reinforcement Learning (DRL) based algorithm, namely Federated Dueling Double Deep Q-Network (FD3QN), with the objective of minimizing the weighted sum of service latency and energy consumption. Horizontal federated learning is incorporated into the Dueling Double Deep Q-Network (D3QN) to allocate cross-domain resources after the offload decision process. A client-server framework with federated aggregation is used to maintain the global model. The proposed FD3QN algorithm can jointly optimize power, sub-band, and computational resources. Simulation results show that the proposed algorithm outperforms baselines in terms of system cost and exhibits better robustness in uncertain IoV environments.

由于车联网（Internet of vehicle, IoV）中服务请求的动态性和服务分布的不均匀性，预装服务器的多接入边缘计算（Multi-access Edge Computing， MEC）网络在某些时间或某些区域往往存在计算能力不足的问题。此外，车辆用户（vu）需要共享他们的观察结果以进行集中神经网络训练，从而导致额外的通信开销。在本文中，我们提出了一种混合MEC服务器架构，其中固定路边单元（rsu）和移动边缘服务器（MESs）合作为vu提供计算卸载服务。我们提出了一种基于分布式联邦学习和深度强化学习（DRL）的算法，即federated Dueling Double Deep Q-Network (FD3QN)，其目标是最小化服务延迟和能量消耗的加权总和。将水平联邦学习引入Dueling双深度Q-Network (D3QN)，在卸载决策过程后进行跨域资源分配。使用具有联邦聚合的客户机-服务器框架来维护全局模型。提出的FD3QN算法可以共同优化功率、子带和计算资源。仿真结果表明，该算法在系统成本方面优于基线，在不确定的车联网环境中具有更好的鲁棒性。

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引用次数: 0

Towards parallel Metaverse: Symbiosis of physical and virtual worlds based on Cybertwin-enabled 6G 迈向平行的虚拟世界：基于赛博双胞胎的6G的物理和虚拟世界的共生

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.05.011

Kaiyue Luo , Yumei Wang , Yu Liu , Jiake Li , Jishiyu Ding , Kewu Sun

Metaverse, envisioned as the next evolution of the Internet, is expected to evolve into an innovative medium advancing information civilization. Its core characteristics, including ubiquity, seamlessness, immersion, interoperability and metaspatiotemporality, are catalyzing the development of multiple technologies and fostering a convergence between the physical and virtual worlds. Despite its potential, the critical concept of symbiosis, which involves the synchronous generation and management of virtuality from reality and serves as the cornerstone of this convergence, is often overlooked. Additionally, cumbersome service designs, stemming from the intricate interplay of various technologies and inefficient resource utilization, are impeding an ideal Metaverse ecosystem. To address these challenges, we propose a bi-model Parallel Symbiotic Metaverse (PSM) system, engineered with a Cybertwin-enabled 6G framework where Cybertwins mirror Sensing Devices (SDs) and serve a bridging role as autonomous agents. Based on this framework, the system is structured into two models. In the queue model, SDs capture environmental data that Cybertwins then coordinate and schedule. In the service model, Cybertwins manage service requests and collaborate with SDs to make responsive decisions. We incorporate two algorithms to address resource scheduling and virtual service responses, showcasing the synergistic role of Cybertwins. Moreover, our PSM system advocates for the participation of SDs from collaborators, enhancing performance while reducing operational costs for Virtual Service Operator (VSO). Finally, we comparatively analyze the efficiency and complexity of the proposed algorithms, and demonstrate the efficacy of the PSM system across multiple performance indicators. The results indicate our system can be deployed cost-effectively with Cybertwin-enabled 6G.

被认为是因特网的下一个发展方向的“虚拟世界”将成为推进信息文明的创新媒介。它的核心特征，包括无处不在、无缝、沉浸式、互操作性和超时空性，正在促进多种技术的发展，并促进物理世界和虚拟世界之间的融合。尽管它的潜力，共生的关键概念，涉及从现实中同步产生和管理虚拟，并作为这种融合的基石，经常被忽视。此外，由于各种技术错综复杂的相互作用和低效的资源利用，繁琐的服务设计正在阻碍理想的元宇宙生态系统。为了应对这些挑战，我们提出了一个双模型并行共生元宇宙（PSM）系统，该系统采用支持cybertwin的6G框架，其中cybertwin镜像传感设备（sd）并作为自主代理充当桥接角色。在此框架下，系统被分为两个模型。在队列模型中，sd捕获环境数据，然后Cybertwins对其进行协调和调度。在服务模型中，Cybertwins管理服务请求，并与软件客户端协作以做出响应性决策。我们结合了两种算法来解决资源调度和虚拟服务响应，展示了Cybertwins的协同作用。此外，我们的PSM系统提倡合作者的SDs参与，在提高性能的同时降低虚拟服务运营商（VSO）的运营成本。最后，我们比较分析了所提出算法的效率和复杂性，并证明了PSM系统在多个性能指标上的有效性。结果表明，我们的系统可以经济有效地与支持cybertwin的6G一起部署。

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引用次数: 0

Reconfigurable intelligent surface-aided dual-function radar and communication systems with MU-MIMO communication 具有MU-MIMO通信的可重构智能地面辅助双功能雷达和通信系统

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.06.013

Yasheng Jin , Hong Ren , Cunhua Pan , Zhiyuan Yu , Ruisong Weng , Boshi Wang , Gui Zhou , Yongchao He , Maged Elkashlan

In this paper, we investigate an reconfigurable intelligent surface-aided Integrated Sensing And Communication (ISAC) system. Our objective is to maximize the achievable sum rate of the multi-antenna communication users through the joint active and passive beamforming. Specifically, the weighted minimum mean-square error method is first used to reformulate the original problem into an equivalent one. Then, we utilize an alternating optimization algorithm to decouple the optimization variables and decompose this challenging problem into two subproblems. Given reflecting coefficients, a penalty-based algorithm is utilized to deal with the non-convex radar Signal-to-Noise Ratio (SNR) constraints. For the given beamforming matrix of the base station, we apply majorization-minimization to transform the problem into a Quadratic Constraint Quadratic Programming (QCQP) problem, which is ultimately solved using a Semi-Definite Relaxation (SDR) based algorithm. Simulation results illustrate the advantage of deploying reconfigurable intelligent surface in the considered multi-user Multiple-Input Multiple-Output (MIMO) ISAC systems.

本文研究了一种可重构智能地面辅助集成传感与通信（ISAC）系统。我们的目标是通过联合主动和被动波束形成，使多天线通信用户的可实现和速率最大化。具体而言，首先利用加权最小均方误差法将原问题重新表述为等效问题。然后，我们利用交替优化算法将优化变量解耦，并将这个具有挑战性的问题分解为两个子问题。在给定反射系数的情况下，采用基于惩罚的算法来处理非凸雷达信噪比约束。对于给定的基站波束形成矩阵，采用最大极小化方法将问题转化为二次约束二次规划（QCQP）问题，最终采用基于半确定松弛（SDR）的算法求解。仿真结果说明了在考虑的多用户多输入多输出（MIMO） ISAC系统中部署可重构智能表面的优势。

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引用次数: 0

Resilient task offloading in integrated satellite-terrestrial networks with mobility-induced variability 具有移动诱导变异的卫星-地面综合网络中的弹性任务卸载

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.07.004

Kongyang Chen , Guomin Liang , Hongfa Zhang , Waixi Liu , Jiaxing Shen

Low Earth Orbit (LEO) satellites have gained significant attention for their low-latency communication and computing capabilities but face challenges due to high mobility and limited resources. Existing studies integrate edge computing with LEO satellite networks to optimize task offloading; however, they often overlook the impact of frequent topology changes, unstable transmission links, and intermittent satellite visibility, leading to task execution failures and increased latency. To address these issues, this paper proposes a dynamic integrated space-ground computing framework that optimizes task offloading under LEO satellite mobility constraints. We design an adaptive task migration strategy through inter-satellite links when target satellites become inaccessible. To enhance data transmission reliability, we introduce a communication stability constraint based on transmission bit error rate (BER). Additionally, we develop a genetic algorithm (GA)-based task scheduling method that dynamically allocates computing resources while minimizing latency and energy consumption. Our approach jointly considers satellite computing capacity, link stability, and task execution reliability to achieve efficient task offloading. Experimental results demonstrate that the proposed method significantly improves task execution success rates, reduces system overhead, and enhances overall computational efficiency in LEO satellite networks.

近地轨道卫星因其低延迟通信和计算能力而备受关注，但由于高移动性和资源有限而面临挑战。现有研究将边缘计算与低轨道卫星网络相结合，优化任务卸载；然而，它们往往忽略了频繁的拓扑变化、不稳定的传输链路和间歇性卫星可见性的影响，从而导致任务执行失败和延迟增加。为了解决这些问题，本文提出了一个动态集成的空间-地面计算框架，该框架优化了LEO卫星移动约束下的任务卸载。在目标卫星不可达的情况下，设计了一种基于星间链路的自适应任务迁移策略。为了提高数据传输的可靠性，引入了基于传输误码率的通信稳定性约束。此外，我们开发了一种基于遗传算法（GA）的任务调度方法，在最小化延迟和能耗的同时动态分配计算资源。该方法综合考虑了卫星计算能力、链路稳定性和任务执行可靠性，实现了高效的任务卸载。实验结果表明，该方法显著提高了低轨道卫星网络任务执行成功率，降低了系统开销，提高了整体计算效率。

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引用次数: 0

Model layered optimization with contrastive learning for personalized federated learning 基于对比学习的个性化联邦学习模型分层优化

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.08.011

Dawei Xu , Chentao Lu , TianXin Chen , Baokun Zheng , Chuan Zhang , Liehuang Zhu , Jian Zhao

In federated learning (FL), the distribution of data across different clients leads to the degradation of global model performance in training. Personalized Federated Learning (pFL) can address this problem through global model personalization. Researches over the past few years have calibrated differences in weights across the entire model or optimized only individual layers of the model without considering that different layers of the whole neural network have different utilities, resulting in lagged model convergence and inadequate personalization in non-IID data. In this paper, we propose model layered optimization for feature extractor and classifier (pFedEC), a novel pFL training framework personalized for different layers of the model. Our study divides the model layers into the feature extractor and classifier. We initialize the model's classifiers during model training, while making the local model's feature extractors learn the representation of the global model's feature extractors to correct each client's local training, integrating the utilities of the different layers in the entire model. Our extensive experiments show that pFedEC achieves 92.95% accuracy on CIFAR-10, outperforming existing pFL methods by approximately 1.8%. On CIFAR-100 and Tiny-ImageNet, pFedEC improves the accuracy by at least 4.2%, reaching 73.02% and 28.39%, respectively.

在联邦学习（FL）中，数据在不同客户端的分布会导致训练中全局模型性能的下降。个性化联邦学习（pFL）可以通过全局模型个性化来解决这个问题。过去几年的研究对整个模型的权重差异进行了校准，或者只对模型的个别层进行了优化，而没有考虑到整个神经网络的不同层具有不同的效用，导致模型收敛滞后，非iid数据的个性化不足。本文提出了针对特征提取器和分类器（pFedEC）的模型分层优化，这是一种针对模型不同层进行个性化的新型pFL训练框架。我们的研究将模型层分为特征提取层和分类器层。我们在模型训练期间初始化模型的分类器，同时使局部模型的特征提取器学习全局模型的特征提取器的表示来纠正每个客户端的局部训练，整合整个模型中不同层的效用。我们的大量实验表明，pFedEC在CIFAR-10上的准确率达到92.95%，比现有的pFL方法高出约1.8%。在CIFAR-100和Tiny-ImageNet上，pFedEC的准确率分别达到73.02%和28.39%，至少提高了4.2%。

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引用次数: 0

Radio map estimation using a CycleGAN-based learning framework for 6G wireless communication 使用基于cyclegan的6G无线通信学习框架的无线电地图估计

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.08.001

Yilin Ma , Chiya Zhang , Chunlong He , Xingquan Li

As the 6G era approaches, wireless communication faces challenges such as massive user numbers, high mobility, and spectrum resource sharing. Radio maps are crucial for network design, optimization, and management, providing essential channel information. In this paper, we propose an innovative learning framework for Radio Map Estimation (RME) based on cycle-consistent generative adversarial networks. Traditional RME methods are often constrained by model complexity and interpolation accuracy, while learning-based methods require strictly paired datasets, making their practical application difficult. Our method overcomes these limitations by enabling training with unpaired data, efficiently converting local features into radio maps. Our experimental results demonstrate the effectiveness of the proposed method in two scenarios: accurate map data and map data with dynamic errors. To address dynamic interference, we designed a two-stage learning process that uses sparse observations to correct local details in the radio map, and the model's accuracy and practicality.

随着6G时代的到来，无线通信面临着海量用户、高移动性、频谱资源共享等挑战。无线电地图对网络设计、优化和管理至关重要，它提供了必要的信道信息。在本文中，我们提出了一种创新的基于循环一致生成对抗网络的无线电地图估计（RME）学习框架。传统的RME方法往往受到模型复杂性和插值精度的限制，而基于学习的方法需要严格配对的数据集，这给实际应用带来了困难。我们的方法通过使用非配对数据进行训练，有效地将局部特征转换为无线电地图，从而克服了这些限制。实验结果证明了该方法在精确地图数据和动态误差地图数据两种情况下的有效性。为了解决动态干扰，我们设计了一个两阶段的学习过程，使用稀疏观测来纠正无线电地图中的局部细节，以及模型的准确性和实用性。

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引用次数: 0

Cybertwin driven resource allocation using optimized proximal policy based federated learning in 6G enabled edge environment 在支持6G的边缘环境中，使用优化的基于近端策略的联合学习的网络孪生驱动的资源分配

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.05.015

Sowmya Madhavan , M.G. Aruna , G.P. Ramesh , Abdul Lateef Haroon Phulara Shaik , Dhulipalla Ramya Krishna

Sixth-generation (6G) communication system promises unprecedented data density and transformative applications over different industries. However, managing heterogeneous data with different distributions in 6G-enabled multi-access edge cloud networks presents challenges for efficient Machine Learning (ML) training and aggregation, often leading to increased energy consumption and reduced model generalization. To solve this problem, this research proposes a Weighted Proximal Policy-based Federated Learning approach integrated with ResNet50 and Scaled Exponential Linear Unit activation function (WPPFL-RS). The proposed method optimizes resource allocation such as CPU and memory, through enhancing the Cyber-twin technology to estimate the computing capacities of edge clouds. The proposed WPPFL-RS approach significantly minimizes the latency and energy consumption, solving complex challenges in 6G-enabled edge computing. This makes sure that efficient resource utilization and enhanced performance in heterogeneous edge networks. The proposed WPPFL-RS achieves a minimum latency of 8.20 s on 100 tasks, a significant improvement over the baseline Deep Reinforcement Learning (DRL), which recorded 11.39 s. This approach highlights its potential to enhance resource utilization and performance in 6G edge networks.

第六代（6G）通信系统承诺前所未有的数据密度和不同行业的变革性应用。然而，在支持6g的多访问边缘云网络中管理具有不同分布的异构数据对高效机器学习（ML）训练和聚合提出了挑战，这通常会导致能耗增加和模型泛化降低。为了解决这一问题，本研究提出了一种结合ResNet50和缩放指数线性单元激活函数（WPPFL-RS）的加权近端策略联邦学习方法。该方法通过增强Cyber-twin技术来估计边缘云的计算能力，从而优化CPU和内存等资源的分配。提出的WPPFL-RS方法显著降低了延迟和能耗，解决了支持6g的边缘计算中的复杂挑战。这确保了异构边缘网络中有效的资源利用和增强的性能。提出的WPPFL-RS在100个任务上实现了8.20秒的最小延迟，比基线深度强化学习（DRL）的11.39秒有了显着改善。这种方法突出了其在6G边缘网络中提高资源利用率和性能的潜力。

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引用次数: 0

Networked control with guaranteed performance for IoT rehabilitation robot under nonvanishing uncertainties and input quantization 基于非消失不确定性和输入量化的物联网康复机器人网络控制

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.05.003

Shilei Tan , Xuesong Wang , Haoquan Zhou, Wei Gong

The Internet of Things (IoT) technology provides data acquisition, transmission, and analysis to control rehabilitation robots, encompassing sensor data from the robots as well as lidar signals for trajectory planning (desired trajectory). In IoT rehabilitation robot systems, managing nonvanishing uncertainties and input quantization is crucial for precise and reliable control performance. These challenges can cause instability and reduced effectiveness, particularly in adaptive networked control. This paper investigates networked control with guaranteed performance for IoT rehabilitation robots under nonvanishing uncertainties and input quantization. First, input quantization is managed via a quantization-aware control design, ensur stability and minimizing tracking errors, even with discrete control inputs, to avoid chattering. Second, the method handles nonvanishing uncertainties by adjusting control parameters via real-time neural network adaptation, maintaining consistent performance despite persistent disturbances. Third, the control scheme guarantees the desired tracking performance within a specified time, with all signals in the closed-loop system remaining uniformly bounded, offering a robust, reliable solution for IoT rehabilitation robot control. The simulation verifies the benefits and efficacy of the proposed control strategy.

物联网（IoT）技术为控制康复机器人提供数据采集、传输和分析，包括来自机器人的传感器数据以及用于轨迹规划（期望轨迹）的激光雷达信号。在物联网康复机器人系统中，管理不消失的不确定性和输入量化对于精确可靠的控制性能至关重要。这些挑战可能导致不稳定和有效性降低，特别是在自适应网络控制中。研究了非消失不确定性和输入量化条件下物联网康复机器人性能保证的网络化控制。首先，输入量化通过量化感知控制设计进行管理，确保稳定性和最小化跟踪误差，即使是离散控制输入，也可以避免抖振。其次，该方法通过实时神经网络自适应调整控制参数来处理非消失的不确定性，在持续干扰下保持一致的性能。第三，控制方案保证了在规定时间内的理想跟踪性能，闭环系统中所有信号保持一致有界，为物联网康复机器人控制提供了鲁棒、可靠的解决方案。仿真结果验证了该控制策略的有效性和有效性。

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引用次数: 0

Enabling intent-driven CoX mechanism in space-terrestrial network for multiple mission impossible 实现空间-地面网络中多种不可能任务的意图驱动的CoX机制

IF 7.5 2区计算机科学 Q1 TELECOMMUNICATIONS

Digital Communications and Networks

Pub Date : 2025-12-01 DOI: 10.1016/j.dcan.2025.06.009

Ying Ouyang, Chungang Yang, Rongqian Fan, Tangyi Li

The Space-Terrestrial Network (STN) aims to deliver comprehensive on-demand network services, addressing the broad and varied needs of Internet of Things (IoT) applications. However, the STN faces new challenges such as service multiplicity, topology dynamicity, and conventional management complexity. This necessitates a flexible and autonomous approach to network resource management to effectively align network services with available resources. Thus, we incorporate the Intent-Driven Network (IDN) into the STN, enabling the execution of multiple missions through automated resource allocation and dynamic network policy optimization. This approach enhances programmability and flexibility, facilitating intelligent network management for real-time control and adaptable service deployment in both traditional and IoT-focused scenarios. Building on previous mechanisms, we develop the intent-driven CoX resource management model, which includes components for coordination intent decomposition, collaboration intent management, and cooperation resource management. We propose an advanced intent verification mechanism and create an intent-driven CoX resource management algorithm leveraging a two-stage deep reinforcement learning method to minimize resource usage and delay costs in cross-domain communications within the STN. Ultimately, we establish an intent-driven CoX prototype to validate the efficacy of this proposed mechanism, which demonstrates improved performance in intent refinement and resource management efficiency.

空间-地面网络（STN）旨在提供全面的按需网络服务，满足物联网（IoT）应用的广泛和多样化需求。但是，STN面临着业务多样性、拓扑动态性和传统管理复杂性等新的挑战。这就需要一种灵活和自主的网络资源管理方法，以便有效地将网络服务与可用资源结合起来。因此，我们将意图驱动网络（IDN）整合到STN中，通过自动资源分配和动态网络策略优化来实现多个任务的执行。这种方法增强了可编程性和灵活性，有助于在传统和物联网场景下实现实时控制和适应性业务部署的智能网络管理。在先前机制的基础上，我们开发了意图驱动的CoX资源管理模型，该模型包括用于协调意图分解、协作意图管理和合作资源管理的组件。我们提出了一种先进的意图验证机制，并利用两阶段深度强化学习方法创建了一个意图驱动的CoX资源管理算法，以最大限度地减少STN内跨域通信中的资源使用和延迟成本。最后，我们建立了一个意图驱动的CoX原型来验证该机制的有效性，该机制在意图细化和资源管理效率方面证明了改进的性能。

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引用次数: 0